Ready-to-use public infrastructure for global SARS-CoV-2 monitoring

Abstract

To the Editor -The COVID-19 pandemic is the first health crisis characterized by large amounts of genomic data 1 . Computational infrastructure can be a bottleneck for data analysis, amplifying global inequalities in ability to track SARS-CoV-2 evolution. This is an issue even in developed countries, as computational infrastructure requires expertise in resource procurement, configuration and maintenance. Commercial computational clouds do not fully address the problem because these resources must still be configured and funded. Furthermore, commercial clouds are predominantly US-based and many countries have policies making payments to foreign providers impractical. In developing countries, research computing infrastructure is rare and researchers often cannot afford commercial cloud-based computation. Here, we present the COVID-19 effort by the Galaxy Project, which pools free worldwide public computational infrastructure, making the analysis of deep sequencing data accessible to anyone while also providing an analytical framework for global pathogen genomic surveillance based on raw sequencing-read data.Despite the existence of well designed and validated SARS-CoV-2 data analysis approaches 2,3 , the ad hoc 4 nature of their application often complicates the integration and comparison of analysis results. Public computational infrastructure (XSEDE, ELIXIR and Nectar Cloud in the United States, European Union and Australia, respectively) coupled with existing open-source software offers a solution to SARS-CoV-2 analytics challenges. However, glue is required to bind these resources into a unified platform for managing users, allocating storage and pairing analysis tools with appropriate computational resources. Such a platform is best not developed by a single principal investigator, group or institution, but rather supported by an international community of users, developers and educators.We have developed a two-stage platform (Fig. 1) housed on three public Galaxy instances 5 in the United States (http:// usegalaxy.org), the European Union (http:// usegalaxy.eu) and Australia (http://usegalaxy. org.au) and capable of supporting hundreds of thousands of complex analyses per month. Anyone can run effectively unlimited

Design of SARS-CoV-2 Variant-Specific PCR Assays Considering Regional and Temporal Characteristics

“…Global genomic databases for emergent variants have greatly improved since the onset of COVID-19 pandemics ( 31 ). Before COVID-19, influenza virus sequences are archived in GISAID.…”

Selection Analysis Identifies Clusters of Unusual Mutational Changes in Omicron Lineage BA.1 That Likely Impact Spike Function

“… Intrapatient allelic variation seen at BA.1 amino acid mutation sites in a subset of SARS-CoV-2 raw sequencing data since March 2020 analyzed using a standardized variant calling pipeline ( Maier et al 2021 ). The areas of the circles indicate the proportions of raw sequence data sets (per 1,000 samples) where a mutation away from the Wuhan-Hu-1 consensus sequence was called.…”

“…Intrahost allelic variation seen at BA.1 amino acid mutation sites was analyzed in 282,788 annotated (i.e., with detailed associated metadata) publically available SARS-CoV-2 raw sequencing data sets from the UK, Greece, Estonia, Ireland, and South Africa between March 2020 and September 2021 all of which were processed and analyzed using the standardized variant calling pipeline described in Maier et al (2021) . All variant calling data for genomic sites where BA.1, 2, and 3 lineage-defining mutations occur were extracted from processed data sets available via ftp://xfer13.crg.eu/ and https://covid19.galaxyproject.org/genomics/global_platform/#processed-cog-uk-data can be explored using the observable notebook at https://observablehq.com/@spond/intrahost-dashboard .…”

Planemo: a command-line toolkit for developing, deploying, and executing scientific data analyses

“…Galaxy is traditionally accessed via a graphical interface in the web browser, and features such as Galaxy collections already provide a high level of parallelization to users of the graphical interface. Nonetheless, there are important scenarios in which a user might need to run individual workflows hundreds or thousands of times, in which the data cannot be grouped into collections ahead of time-for example, for variant calling of SARS-CoV-2 genomic data, in which a huge amount of new data is published continuously [28]. As a convenient alternative to the graphical interface, Planemo allows workflow execution to be scheduled programmatically using the `run` subcommand, either on a local machine or a larger Galaxy server.…”